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Model facts & API quick start
Z-Image Turbo (z-image-turbo) is served on Chutes inside a Trusted Execution Environment (TEE). Call it at https://vonkaiser-z-image-turbo.chutes.ai/generate with a Chutes API key.
Z-Image Turbo at a glance
- Model ID
z-image-turbo- Deployed by
- vonkaiser
- Type
- Image
- Compute price
- $2.25 per hour
- Modalities
- text in → image out
- Confidential compute (TEE)
- Yes, runs in a Trusted Execution Environment
- Open weights
Tongyi-MAI/Z-Image-Turbo- Parameters
- 6B
- Architecture
- Scalable Single-Stream Diffusion Transformer (S3-DiT) with a Qwen3 text encoder and flow-matching scheduler
- License
- Apache-2.0
- Released
- 2025-11
- Transformer
- 30 layers, hidden dim 3840, 30 attention heads
- Distillation
- 8 NFEs (function evaluations); CFG disabled
- Scheduler
- FlowMatchEulerDiscreteScheduler
- Text rendering
- Bilingual (English and Chinese), per model card
- Output format on Chutes
- image/png
API quick start
- Base URL
https://vonkaiser-z-image-turbo.chutes.ai- Endpoint
POST /generate- Auth header
Authorization: Bearer $CHUTES_API_KEY
curl -X POST "https://vonkaiser-z-image-turbo.chutes.ai/generate" \
-H "Authorization: Bearer $CHUTES_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"seed": 42,
"shift": 3,
"width": 1024,
"height": 1024,
"prompt": "a serene mountain lake at sunset",
"guidance_scale": 0,
"max_sequence_length": 512,
"num_inference_steps": 9
}' \
--output output.pngZ-Image Turbo FAQ
How many inference steps should I use with Z-Image Turbo?
The model is distilled for 8 function evaluations, and the chute defaults to 9 steps. Stick close to the default; unlike standard diffusion models, raising the step count substantially does not buy proportional quality because the distillation targets a fixed short schedule.
Why is guidance_scale 0, and can I use a negative prompt?
Turbo is distilled with classifier-free guidance baked out, so the chute defaults guidance_scale to 0 and exposes no negative prompt field. Express exclusions inside the positive prompt. If you need CFG and negative prompting, the non-distilled base Z-Image model supports both.
What license is Z-Image Turbo under? Can I use it commercially?
Apache-2.0, per the Hugging Face repo Tongyi-MAI/Z-Image-Turbo. Apache-2.0 permits commercial use, modification, and redistribution with attribution and license notice.
How do I call this chute from code?
POST a flat JSON body to https://vonkaiser-z-image-turbo.chutes.ai/generate with an Authorization: Bearer header carrying your Chutes API key. The response body is the PNG image itself, not JSON, so write it straight to a file or blob.
Can it render text inside images?
Yes. Bilingual text rendering (English and Chinese) is one of the model card's headline capabilities. Quote the exact text you want in the prompt and keep it short for best fidelity.
What image sizes does it support?
The chute defaults to 1024x1024 and accepts width and height fields. Keep dimensions moderate while iterating; larger canvases cost more compute and take longer.
Is Z-Image Turbo an editing model?
No, it is text-to-image only. The Z-Image family includes a separate Z-Image-Edit variant for instruction-based editing, and Chutes hosts dedicated edit models such as Qwen-Image-Edit-2511.